Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid .

HMGB1 binding to receptor for advanced glycation end products enhances inflammatory responses of human bronchial epithelial cells by activating p38 MAPK and ERK1/2.

The proinflammatory factor high mobility group box protein 1 (HMGB1) has been implicated as an important mediator of many chronic inflammatory diseases, including asthma. Human bronchial epithelial cells (HBECs) play a central role in the pathogenesis of asthma. However, the effects of HMGB1 on HBECs and the underlying mechanisms remain unknown. Here, we investigated receptor expression and proinflammatory cytokine production by primary cultures of HBECs stimulated by HMGB1. We then examined the effects of specific receptor blockade and inhibition of p38 MAPK, ERK1/2, or PI3-K on HMGB1-induced expression of proinflammatory cytokines. HMGB1 increased the expression and secretion of TNF-α, TSLP, MMP-9, and VEGF in a dose- and time-dependent manner. HMGB1 also induced elevated expression of RAGE protein. Secretion of TNF-α, VEGF, MMP-9, and TSLP was significantly decreased by RAGE blockade and p38 MAPK pathway inhibition, while a less pronounced effect was mediated by ERK1/2 inhibition. These observations suggest that HMGB1 binds RAGE and promotes activities of p38 MAPK and ERK1/2 pathways in HBECs. This then enhances the expression of TNF-α, VEGF, MMP-9, and TSLP, which are the important inflammatory factors in asthma. These results demonstrate that HMGB1 enhances the inflammatory responses of HBECs, which are involved in the modulation of inflammatory processes in asthma.

HMGB1 contributes to allergen-induced airway remodeling in a murine model of chronic asthma by modulating airway inflammation and activating lung fibroblasts.

The pro-inflammation factor high-mobility group box protein 1 (HMGB1) has been implicated in the pathogenesis of asthma. In this study, we used a murine model of chronic asthma to evaluate the effects of HMGB1 on airway remodeling. Female BALB/c mice were randomly divided into four groups: control, ovalbumin (OVA) asthmatic, OVA+isotype antibody and OVA+anti-HMGB1 antibody. Anti-HMGB1 antibody therapy was started on day 21 and was administered three times per week for 6 weeks before intranasal challenge with OVA. In this mouse model, HMGB1 expression is significantly elevated. The anti-HMGB1 antibody group exhibited decreased levels of immunoglobulin E (IgE) and inflammatory mediators and reduced inflammatory cell accumulation, airway hyperresponsiveness (AHR), mucus synthesis, smooth muscle thickness and lung collagen content compared with the OVA groups. Treatment with HMGB1 increased proliferation, migration, collagen secretion and α-smooth muscle actin (SMA) expression in MRC-5 cells. Treatment with the HMGB1/IL-1ß complex significantly increased the expression and secretion of transforming growth factor (TGF-ß1), matrix metalloproteinase (MMP)-9 and vascular endothelial growth factor (VEGF). Altogether, these results suggest that blocking HMGB1 activity may reverse airway remodeling by suppressing airway inflammation and modulating lung fibroblast phenotype and activation.